Label-free fluorescence lifetime imaging for the assessment of cell viability in living tumor fragments

Abstract

Significance: To enable non-destructive longitudinal assessment of drug agents in intact tumor tissue without the use of disruptive probes, we have designed a label-free method to quantify the health of individual tumor cells in excised tumor tissue using multiphoton fluorescence lifetime imaging microscopy (MP-FLIM).

Aim: Using murine tumor fragments which preserve the native tumor microenvironment, we seek to demonstrate signals generated by the intrinsically fluorescent metabolic co-factors nicotinamide adenine dinucleotide phosphate [NAD(P)H] and flavin adenine dinucleotide (FAD) correlate with irreversible cascades leading to cell death.

Approach: We use MP-FLIM of NAD(P)H and FAD on tissues and confirm viability using standard apoptosis and live/dead (Caspase 3/7 and propidium iodide, respectively) assays.

Results: Through a statistical approach, reproducible shifts in FLIM data, determined through phasor analysis, are shown to correlate with loss of cell viability. With this, we demonstrate that cell death achieved through either apoptosis/necrosis or necroptosis can be discriminated. In addition, specific responses to common chemotherapeutic treatment inducing cell death were detected.

Conclusions: These data demonstrate that MP-FLIM can detect and quantify cell viability without the use of potentially toxic dyes, thus enabling longitudinal multi-day studies assessing the effects of therapeutic agents on tumor fragments.

Keywords: apoptosis; cancer; cell death; fluorescence lifetime; fluorescence lifetime microscopy; metabolism; multiphoton; necroptosis; nicotinamide adenine dinucleotide phosphate.

Fig 1

Baseline viability screening of CT26 LTFs. (a) PI intensity images overlaid with Voronoi segmented cells. (b) Corresponding NMS images. (c) Ten-fold cross-validation AUROC analysis using the NMS KDEs from panel (f) ($n>16,000$ cells). (d) Percentage of cells marked dead using PI (horizontal axis) versus NMS (vertical axis) across 30 fragments. While most fragments contained $>90\%$ living cells, we chose four fragments which represent increasing levels of PI staining, represented with the specific color scheme outlined in panel (a) (leftmost text). (e) Side-by-side comparison of viability obtained using the NMS versus PI ($n=30$). (f) Probability distribution functions of NMS obtained for all PI-positive cells versus PI-negative cells across all 30 fragments ($n>\mathrm{16,000}$ cells). Average and standard deviation optimal threshold values were retrieved for each fold via AUROC analysis and are visualized by a vertical green solid line in panel (f).

Fig 2

NMS assessment across various mechanisms of cell death in CT26 LTFs—validated against PI (cell death ground truth). (a) NMS images following treatment with different chemicals (${\mathrm{H}}_2{\mathrm{O}}_2$, staurosporine, and shikonin) or conditions (heat shock) compared with untreated control (vehicle). (b) Corresponding images of PI exogenous staining. (c) and (d) Ten-fold cross-validation AUROC analysis using averaged NMS per cell region marked alive or dead via PI staining. AUC analysis for all necrosis and apoptosis-inducing agents is shown in panel (c), whereas AUC analysis for shikonin-induced necroptosis is provided in panel (d). (e) Confusion matrix comparison of dead/alive identification using PI stain or by label-free NMS. (f)–(j) KDE distributions of NMS obtained across the entire field of view (FOV) before and after being subjected to treatment. Importantly, photon count thresholding was undertaken to reject pixels with an insufficient signal from subsequent analysis (see Sec. 2).

Fig 3

NMS assessment across various mechanisms of cell death in CT26 LTFs—validated against Caspase 3/7 Red (apoptosis). (a) NMS images following treatment with different chemicals (staurosporine and shikonin) or conditions (heat shock) compared with untreated control (vehicle). (b) Corresponding images of Caspase 3/7 Red exogenous staining. (c) NAD(P)H and FAD, and their proportional representation pre- and post-treatment, summarized into phasor plots (described in Sec. 2) where the values in each pixel are plotted, forming a cloud of data points in the plot. Each treatment’s impact on the FLIM signature is marked by a distinct shift in the phasor cloud compared with baseline measurement.

Fig 4

NMS assessment across various mechanisms of cell death in MCA205 LTFs—validated against PI. (a) NMS images following treatment with different chemicals (staurosporine and shikonin) or conditions (heat shock) compared with untreated control (vehicle). (b) Corresponding images of PI exogenous staining. (c) and (d) Ten-fold cross-validation AUROC analysis using averaged NMS per cell region marked alive or dead via PI staining. AUC analysis for all necrosis and apoptosis-inducing agents is shown in panel (c), where AUC analysis for shikonin-induced necroptosis is provided in panel (d). (e) Confusion matrix comparison of dead/alive identification using PI stain or by label-free NMS using values obtained in separate syngeneic tumor models (i.e., CT26—see Figs. 1 and 3). (f)–(i) KDE distributions of NMS obtained across the entire FOV of fragments before and after being subjected to treatment.

Fig 5

NMS assessment of chemotherapeutic efficacy in CT26 murine LTFs. (a) and (b) NMS and PI images of LTFs treated with either vehicle (a) or with a combination of 3-MA and doxorubicin (b). KDE distributions of NMS obtained across the entire FOV of fragments at baseline and 24 and 48 h are included for both cases. (c) Viability plot across time post-treatment determined via NMS imaging for vehicle and 3-MA/doxorubicin-treated LTFs ($n=3$ fragments per condition).